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Understanding the Utility of a Reasoner or Inference Engine

A reasoner is software that is able to infer logical consequences from a set of asserted facts. Every reasoner uses some sort of logic.  For example, first-order predicate logic is a type of logic.  Every reasoner works with some set of axioms.  An axiom describes some logical fact.  The capabilities of a reasoner depend on the expressiveness of the kind of logic that the reasoner uses and the axioms provided for the reasoner and logic to work against.

Reasoners are sometimes referred to as inference engines because while, as stated above, reasoners work with asserted facts; reasoners can also use the rule of logic to deduce theorems.  Theorems are indirectly deduced facts.  Theorems are deductions which can be proven by constructing a chain of reasoning by applying axioms.  Basically, a reasoner and an inference engine is the same thing.

A rules engine is also a reasoner. Another name for a reasoner or inference engine or rules engine is semantic reasoner.

An XBRL Formula Processor is basically a reasoner.  Did you realize that?  I will get back to that in a moment.

Clearly a human's capacity to apply logic is greater than a computer's capacity to apply logic.  In fact, computers are machines and really can't think or apply logic.  All that a computer can do is mimic or simulate or emulate a human's ability to think. Some computer programs that mimic human thought or perform some task for humans are called expert systems.  Every expert system uses a reasoner to figure out what that system needs to do for the human and how to do it.

I pointed out that care has to be taken in order to express facts in a form that is safe, reliable, predictable, and repeatable.  There are four catastrophic problems that a computer can run up against;

  • Undecidability (i.e. must be decidable)
  • Infinite loops (i.e. must eliminate possibility of cycles)
  • Unbounded structures or pieces (i.e. must have known set of structures)
  • Unspecific or imprecise logic (i.e. things like fuzzy logic is not allowed in this type of system)

Correctly balancing the expressiveness of a logic and the safety, reliability and predictably of a piece of software to return useful information takes conscious, skillful effort and execution.  Years of experimentation in the area of expert systems and artificial intelligence has yielded invaluable information in achieving this balance.

First-order predicate logic is a formal way of expressing logic in a manner that is machine-readable.

While first-order predicate logic is expressive and powerful in performing work, it is not decidable and other problems can occur. 

PROLOG is an attempt to address issues with first-order logic.  In creating PROLOG, the problem of decidability and cycles was partially addressed by limiting which first-order predicate logic statements can be used to a Horn clause.  But even PROLOG had issues and so further restrictions were made to first-order logic expressed using Horn clauses and Datalog was created.

DATALOG is a restricted subset of PROLOG.  DATALOG is described as a query language based on logic.  People are combining relational databases and DATALOG and creating what they call "deductive databases". Datomic is one such database.  It seems that DATALOG is a de-facto standard deductive query language. (Here is more information on DATALOG.)

The semantic web folks seem to have had a similar evolution.  They started with OWL FULL or older versions of OWL and then created limitations to deal with the problem of decidability.  State-of-the-art semantic web technologies such as OWL 2 DL have been limited to solve the problem of decidability by limiting the logic to SROIQ description logic which is decidable.

OWL 2 DL has a boatload of reasoners.  What I don't understand is the relative expressive power of an OWL 2 reasoner and something like DATALOG.

However, SROIQ description logic does not support expressing mathematical relations.  The reason is, some math is not decidable.  Eventually they will fix that most likely.

Back to XBRL Formula Processors.  An XBRL Formula Processor is generally seen as something that validates XBRL instance facts.  Says so right here in the XBRL Formula 1.0 Specification, see the Abstract section.  But it is becoming pretty clear to me that what an XBRL Formula processor really is, is a business report reasoning engine.  Or rather, that is what it SHOULD be in my opinion.

XBRL Formula has some distinct advantages over something like OWL 2 DL.  The first advantage is that XBRL Formula does math.  The second thing is that XBRL Formula has an understanding of XBRL Dimensions.  That means that not only can XBRL Formula do math, it also supports a dimensional model.

However, there are several deficiencies in XBRL Formula processors:

  • XBRL Formula processors do not support process chaining.  Supporting chaining was discussed but they decided not to do it.  PROLOG and DATALOG support chaining.  Not sure is OWL 2 DL supports chaining.
  • XBRL Formula processors do not understand and use the "general-special" or "alias-essense" standard XBRL arcroles.  Basically, XBRL Formula processors don't understand class relations.
  • XBRL Formula processors are focused on XBRL instances, they don't provide much functionality for working with XBRL taxonomy information.

My personal opinion is that the world would be a better place if something that had the combined functionality of something like DATALOG and an XBRL Formula Processor; if that combined piece of software struck the correct balance between expressive power and safety/reliability/predictability (i.e. it avoided those four logical catastrophes); and if there was a layer build that helped business professionals work with all this stuff effectively and successfully.

Per the law of conservation of complexity and the idea of irreducible complexity; not until this business report reasoner exists can XBRL ever really be usable by the average business professional.  But imagine if such software did exist.  Any business professional could build their own little or even big expert system inexpensively.

Posted on Thursday, July 30, 2015 at 07:37AM by Registered CommenterCharlie in | CommentsPost a Comment

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